2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)最新文献

{"title":"On using fuzzy c-means clustering in the fuzzy signature concept classification of liver lesions","authors":"Melinda Kovács, F. Lilik, S. Nagy, L. Kóczy","doi":"10.1109/ICECCME55909.2022.9988684","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988684","url":null,"abstract":"Liver is a very unique organ, it has double blood supply, not only through the arteries, but also through the veins. This property makes the contrast material enhanced computer tomography images show very characteristic behavior, depending on the time passed from the adjustment of the contrast material. When diagnosing a nodule in the liver by computer tomography, radiologist experts use multiple images with different delay factors, and generally, five basic characteristic properties of the nodule compared to the normal liver tissues. In the following considerations, we give a simplified model that reproduces the way medical experts take decisions, and offer a possibility to develop a computer aided diagnosis method. The classification of the nodules applies a model with fuzzy signatures, where the aggregation functions in the intermediate nodes are representing the radiologist point of view, while the membership degrees/functions at the leaves of the fuzzy signature's rooted tree are obtained from calculations applying the fuzzy c-means clustering algorithm.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115289748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal Wedge Bond Detection Using Convolutional Autoencoders in Industrial Vision Systems","authors":"Ji-Yan Wu, Yatian Pang, Xiang Li, Wenju Lu","doi":"10.1109/ICECCME55909.2022.9987801","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9987801","url":null,"abstract":"Anomaly detection using machine vision images is of utmost importance for mission-critical tasks in industrial applications. Convolutional autoencoders have been widely adopted for unsupervised defect identification on vision data. This type of deep learning vision model is able to identify the anomaly pattern of target objects/areas without incurring the heavy workload on annotation/labelling. This paper investigates the challenging problem of anomaly detection in the quality control of wedge bond production. We report the progress of using SSIM (Structural Similarity)-based autoencoder in the abnormal wedge bond detection. The main idea is to leverage the advantages of both SSIM and autoencoder on identifying the abnormal features of wedge bond. SSIM analyzes the image difference in terms of luminance, contrast and structure. An autoencoder is trained to identify such visual differences in the prediction images. We conduct extensive experiments using images captured from real wedge production line for the anomaly detection. The evaluation results demonstrate the effectiveness and accuracy of the SSIM-based autoencoder.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115686884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new concept of transmitting antenna on bi-static radar for space debris monitoring","authors":"G. Bianchi, M. F. Montaruli, M. Roma, S. Mariotti, P. Di Lizia, A. Maccaferri, L. Facchini, C. Bortolotti, Rebecca Minghetti","doi":"10.1109/ICECCME55909.2022.9988566","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988566","url":null,"abstract":"The aim of this article is to propose a new radar concept for the monitoring of space debris in order to increase the performance with respect to the traditional radars. Once put in operation, the new sensor will be able to improve the space surveillance and tracking services within the sensor network of the EUSST consortium. The architecture is based on past experience of observing space debris using the radar called BIRALES (BIstatic Radar for LEo Survey). The transmitting antenna that is described in this paper will be the new emitter of BIRALES. It is designed to be very flexible for any type of observations, modifying its parameters depending of the observation needs and according to the service to offer (monitoring of fragments, re-entry or for collision avoidance).","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116636579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Adoption of Metaverse Systems: A hybrid SEM - ML Method","authors":"A. Alhamad, K. Alomari, M. Alshurideh, B. Al Kurdi, S. Salloum, A. Q. Al-Hamad","doi":"10.1109/ICECCME55909.2022.9988215","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988215","url":null,"abstract":"seeing high-tech medical devices from other nations and witnessing surgery to learn has become nearly unattainable. The pandemic of coronavirus disease 2019 (COVID-19) has created cross-border medical education challenging. Nevertheless, to cater to the increase in non-face-to-face education, instructional techniques entailing the “metaverse” are being initiated in the medical field, since medical staff from all over the globe who frequented the UAE to acquire skills in medical technology and medical students who require to exercise have already had minimal prospects to collaborate closely with patients attributable to COVID-19. Employing video-conferencing technology like Zoom to provide effective medical education is similarly difficult. The research's goal is to learn the perception of students in the UAE towards the metaverse system (MV) used for medical training. The conceptual model includes The Technology Acceptance Model (TAM) elements and adoption aspects of perceived value. The research's conceptual model, which connects both personal-based traits and technological features, is what makes it novel. Additionally, the novel hybrid analysis approach will be applied in the present research to conduct machine learning (ML) driven structural equation modeling (SEM) evaluation.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"17 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120843612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Nationwide Elections Through Blockchain Ledger, Utilising Encryption Hardware","authors":"Bogomil Alexandrov, E. Kovatcheva","doi":"10.1109/ICECCME55909.2022.9988032","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988032","url":null,"abstract":"Nowadays, the voting systems around the world have not been technologically updated much, despite the availability of modern and applicable technologies, that could easily overcome the main problems of elections. Blockchain ledger is the perfect candidate for implementing the whole election process. It will increase trust of all involved parties, create immutable records, secure the data used in the election process and make sure that “every vote counts”. The election process will be secured and verifiable with full accountability through data encryption, considering the effect of social conformity on collective voting behavior. Securing the election process and election results through technological advancements is a key element of sustaining the democratic processes around the world or develop a “decentralized democracy”. Every democratic society starts its long path of evolution exactly with the election process and thus it is of fundamental importance to secure this process, its results and provide maximum transparency to the population to avoid any misconceptions or even conspiracy theories. The blockchain ledger as a concept could potentially overcome most of the shortcomings of the current election processes used around the world, including e-voting and machine voting - intermediary processes that just replicate the ancient paper ballot voting mechanism, but in wrapped by electronic means. In this process-design proposal paper, the method for election voting, utilizing blockchain ledger overcomes many classical problems in the election process, such as preserving the secret of the vote, immutability, transparency, decentralization of the system, following the “every vote counts” concept and disallowing double voting. Considering various approaches to blockchain based voting, we have concluded that the best method is to implement a private governmental blockchain solution - both for reducing the costs and for allowing for various specific features to be implemented, such as partial public/private ledger. The consensus algorithm problem could easily be solved by implementing a PoS consensus on a large number of participating nodes, such as most citizens could participate as nodes, being part of a broader eID initiative. Such implementation would prove unrealistic to try and take over a network with millions of independent nodes. The present paper aims at providing only a model for blockchain elections and does not provide an off the shelf solution.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127331530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"5G and IoT: How telecom operators can boost innovation in collaboration with HW labs","authors":"P. Nesse, Marlen Hamsund, Eirin Krogstad","doi":"10.1109/ICECCME55909.2022.9988547","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988547","url":null,"abstract":"The next generation network technology 5G will introduce new opportunities for telecom operators and mitigate their declining connectivity revenues. 5G will among other things enable a massive number of Internet of Things (IoT) devices to be connected to its network. This article reviews and discuss the role of IoT Hardware (HW) labs and their potential contribution to the exploration of new 5G/IoT product and service innovations and business models. The development is jointly executed by the telecom operator, startups and other private and public ecosystem partners connected to these IoT HW labs. Our findings include three established IoT HW labs and a fourth that is a candidate to be established in Norway. The research methods applied are desk top studies, interview, and a preliminary survey of potential lab users. A combined agile innovation and IoT ecosystem framework is designed to support the joint exploration process. Here multiple agile steps (prototype, pilot, and pivot) can be realized through startups access to the IoT HW labs. Other ecosystem partners complement with commercial and technological expertise and resources. The telecom operators can orchestrate the commercialization process as well as the long-term performance and robustness of the whole ecosystem. Further research should include joint innovation use cases across different industry verticals where the participation from the ecosystem actors during all agile innovation steps are studied.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127362478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Memristor based Reservoir Network for Chaotic Time Series Prediction","authors":"Md Razuan Hossain, P. Paul, Maisha Sadia, Anur Dhungel, J. Najem, Md. Sakib Hasan","doi":"10.1109/ICECCME55909.2022.9988207","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988207","url":null,"abstract":"Reservoir Computing (RC) is a highly efficient emerging computing concept in machine learning to process temporal signals and has a low training cost compared to the traditional recurrent neural network. At first, the RC system extracts features from the input dataset and then projects the data in the high dimensional space by creating rich reservoir states. There are two conventional RC approaches such as Echo State Network (ESN) and Liquid State Machine (LSM). In this work, we explore a recent volatile memristor-based RC paradigm with spike encoded input which is attractive for its compact hardware implementation. We use four reported volatile memristors in this paradigm along with two reservoir architectures for discrete-time chaotic time series prediction. Chaotic time series is highly sensitive to the initial condition and slightly change in the initial condition causes eventual divergence in the observed outputs. Logistic map and Henon map are chosen as representative examples of well-known one-dimensional and two-dimensional chaotic maps, respectively. The main goal of this work is to explore and compare the performance of four different memristive RC systems using two different reservoir architectures for chaotic time series prediction.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125440338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilevel Fusion of Deep Features for Face Morphing Attack Detection","authors":"Sushma Venktatesh","doi":"10.1109/ICECCME55909.2022.9987842","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9987842","url":null,"abstract":"Face biometric systems are widely deployed in a magnitude of security-related applications, including border control. However, the vulnerability of the Face Recognition Systems(FRS) to various types of attacks is well demonstrated. This work presents a novel approach for face morphing attack detection for a single image scenario by performing a multi-level fusion of deep features. The features are extracted using the pre-trained deep CNNs such as AlexNet, ResNet50. These extracted features are combined at both features and score levels to conclude if the given face image is a morph. The proposed single image Morph Attack Detection (S-MAD) approach is extensively evaluated on the face morphing dataset constructed using five different face morphing generation techniques and three different data mediums. The data mediums including digital, print-scan (re-digitised), print-scan compression (re-digitised and compressed.) Extensive experiments are carried out with intra (same datatype used for training and testing) and inter-evaluation scenarios (cross datatype used for training and testing). Further, the proposed method is compared with the State-Of-The-Art (SOTA) approaches for No reference-based/Single image Morph Attack Detection (S-MAD). The statistical analysis indicates the best performance of the proposed approach in all three different mediums.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122358340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Universal power electronics hardware trainer for teaching the DC grid","authors":"P. V. van Duijsen, D. Zuidervliet","doi":"10.1109/ICECCME55909.2022.9988666","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988666","url":null,"abstract":"Developing a new curriculum for a power electronics course also gives room to implement multiple teaching and examination methods. Traditional teaching using a textbook, oral lectures and a single written examination can be enhanced with multiple new methods for teaching. Not only replacing part of the oral lectures with practical assignments, but also new teaching methods for practicing the theoretical content, can be implemented. Online simulation and design tools are inevitable to prepare for the practical assignments. The paper discusses how an universal power electronics hardware trainer can be used, for multiple laboratory exercises, and some practical examples are elaborated. The aim of the hardware trainer, is to directly show the students, the relation between theory and practice. The hardware should be configurable, to be able to use it for various applications, ranging from dc-dc converters for solar and battery interfacing, inverters for motor control and synchronous buck converter for DC grid manager. The hardware trainer is to be extended with a dedicated control and load board.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122437004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysing High Dimensional Data using Rough Tolerance Relation","authors":"K. Anitha, D. Datta","doi":"10.1109/ICECCME55909.2022.9988257","DOIUrl":"https://doi.org/10.1109/ICECCME55909.2022.9988257","url":null,"abstract":"It is estimated that there is an exponential growth of data and it is being sprawl in many devices and cloud platforms. Organizing these data in proper pattern is an essential task for data scientists. Dimensionality reduction or removal of inconsistent variables is a major task of organizing high dimensional data. Rough set plays an important role in attribute reduction and it finds hidden patterns from the data without expecting additional parameters. This theory was constructed through indiscernibility relation between objects which is an equivalence relation. In this paper we have opted tolerance relation and propose an intelligent tool through rough hybridization technique with neural network. Rough neural network is an essential branch of granular computing and it was introduced by Pawn Lingras [1]. In this paper we propose tolerance based Rough-Neural network algorithm for attribute reduction and this algorithm is being implemented in SECOM data from UCI repository.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}